An adaptive multiple echo canceller for slowly time-varying echopaths

An adaptive structure that cancels multiple echos with slowly time-varying echo path characteristics is presented. The structure consists of two echo path models. One is a primary long-length transversal filter initially used to estimate the echo path in the startup period. The other is a reduced-size filter consisting of several short-length filters used to inherit the significant coefficients of the primary filter after the predetermined conditions are simultaneously satisfied and to function as the echo canceller thereafter. In addition, a monitor/control circuit is used to estimate the time locations of individual dispersive regions from the primary filter and to monitor the short filters to make sure that each one can cover its own dispersive region. With the delays determined, the short filter structure can update the echo path accurately since the number of tap coefficients has been dramatically reduced, increasing the convergence speed. The results of computer simulations utilizing white noise input and speech input are presented     

误差通道建模误差对多误差LMS算法性能的影响

本文证明了，在多通道自适应宽带有源噪声控制中，由于误差通道建模误差的存在，多误差LMS算法是条件收敛的，并推导了保证算法收敛的条件，还证明了，算法收敛后，得到自适应滤波器权值一般只是理论上最优值的有偏估计，计算机仿真结果验证了所得结论．     

On the Convergence of Genetic Scheduling Algorithms for Downlink Transmission in Multi-User MIMO Systems

In a multi-user MIMO system using a successive precoding method such as dirty paper coding, it is combinatorially complex to determine the optimal set of users to schedule and the proper order to encode their signals in order to optimize a utility function in a scheduling algorithm. Genetic algorithms represent a fast suboptimal approach to reducing the complexity of the search. In this paper, we build upon prior work that implements scheduling via genetic algorithms. We examine the impact of parameter values within the adaptive mutation rate of the algorithm on its convergence time. We demonstrate that although there is a range of values for the parameters that yields similar near-minimum convergence times, it is nonetheless important to ensure that the parameters are tuned to be within that range. In one case, tuning the parameter values reduces the time of convergence to less than 30% compared to that achievable with the initial parameter values. We also demonstrate that the proper parameter values are dependent on both the number of transmit antennas and the number of users in the pool of users to be scheduled. A simple equation is proposed that is linear in the adaptive mutation parameters to tune the values for different numbers of transmit antennas and users.     

基于遗传算法的部分自适应波束形成方法

将一种改进的遗传算法用于星载平面阵列天线方向图的实时综合。为了使遗传算法适合实时应用,在唯相自适应阵列的基础上进一步采用了部分阵元和部分比特的自适应波束形成,以降低算法计算量,提高其收敛速度。对基于改进遗传算法的唯相阵列部分阵元自适应波束形成和部分比特自适应波束形成进行了研究。模拟实验结果表明,两种方案都能够在波束“凝视”方向满足一定增益的前提下有效抑制来自旁瓣的强干扰,而且具有比传统唯相算法更快的收敛速度。     

采用合成方法的多路信号自适应时延联合估计

本文关注的是多路信号之间时延差异的联合估计问题。不同于传统的自适应时延估计算法,本文以合成信号作为自适应时延估计的参考信号,给出了基于信号合成的联合自适应时延估计算法。同时本文推导和仿真了该算法时延估计的均值、学习曲线及方差特性。性能分析和仿真结果均显示,本文提出的基于合成的多路信号自适应时延估计为渐进无偏的时延估计。在不明显增加计算量的条件下,当算法收敛时,联合时延估计算法的方差显著低于传统的两路信号之间自适应时延估计算法方差。      

Convergence analysis of an adaptive pseudolinear-regression notch filtering algorithm

This paper analyzes the convergence properties of an adaptive pseudolinear regression notch filtering algorithm recently proposed in the literature for estimating the frequencies of multiple sine waves from measurements corrupted by possibly colored additive noise. Simple necessary and sufficient conditions for the local convergence of this algorithm to the true frequency values are derived. It is shown that the algorithm has an interesting decoupling property in the sense that satisfaction of the convergence condition by a certain frequency implies local convergence to that frequency no matter whether the other frequencies satisfy or do not satisfy the convergence conditions. However, it is also shown that the algorithm is not generally convergent and, therefore, cannot be recommended for widespread use in applications. Numerical examples are used to illustrate the main points in the theoretical analysis.The work of A. Nehorai was supported by the Air Force Office of Scientific Research, under Grant No. AFOSR-90-0164.     

Fast Converging Semi-Blind Space-Time Equalisation for Dispersive QAM MIMO Systems

A novel semi-blind space-time equaliser (STE) is proposed for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, are first utilised to provide a rough initial least squares estimate of the STE?s weight vector. A concurrent gradient-Newton constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast to the minimum mean square error STE solution. Simulation results confirms that the convergence speed of this semi-blind adaptive algorithm is very close to that of the training-based recursive least squares algorithm.     

Blind adaptive receiver for uplink multiuser massive MIMO systems

Herein, we consider uplink multiuser massive multiple‐input multiple‐output systems when multiple users transmit information symbols to a base station (BS) by applying simple space‐time block coding (STBC). At the BS receiver, two detection filters for each user are used to detect the STBC information symbols. One of these filters is for odd‐indexed symbols and the other for even‐indexed symbols. Using constrained output variance metric minimization, we first derive a special relation between the closed‐form optimal solutions for the two detection filters. Then, using the derived special relation, we propose a new blind adaptive algorithm for implementing the minimum output variance‐based optimal filters. In the proposed adaptive algorithm, filter weight vectors are updated only in the region satisfying the special relation. Through a theoretical analysis of the convergence speed and a computer simulation, we demonstrate that the proposed scheme exhibits faster convergence speed and lower steady‐state bit error rate than the conventional scheme.     

一种基于RLS算法的自适应时延估计算法

针对基于LMS算法的自适应时延估计算法收敛速度慢的缺点,推导出一种基于RLS算法的自适应时延估计算法,并分析了基于RLS算法的自适应滤波器的均值性能。经仿真证明该算法的收敛速度有了一定提高,并且其时延估计的跟踪性能得到了改善。     

Proportionate NSAF algorithms with sparseness-measured for acoustic echo cancellation

In acoustic echo cancellation (AEC), the sparseness of impulse responses can vary over time or/and context. For such scenario, the proportionate normalized subband adaptive filter (PNSAF) and μ-law (MPNSAF) algorithms suffer from performance deterioration. To this end, we propose their sparseness-measured versions by incorporating the estimated sparseness into the PNSAF and MPNSAF algorithms, respectively, which can adapt to the sparseness variation of impulse responses. In addition, based on the energy conservation argument, we provide a unified formula to predict the steady-state mean-square performance of any PNSAF algorithm, which is also supported by simulations. Simulation results in AEC have shown that the proposed algorithms not only exhibit faster convergence rate than their competitors in sparse, quasi-sparse and dispersive environments, but also are robust to the variation in the sparseness of impulse responses.     

Adaptive receiver for DS/CDMA communications over impulsive noisechannels

A new adaptive algorithm is proposed for training soft-limiter based correlation receiver in which the direct sequence code division multiple access signals corrupted by impulsive symmetric a-stable noise are demodulated. The new adaptation algorithm allows simpler implementation and faster convergence speed in comparison with the traditional adaptive stochastic gradient-based algorithms     

空间光波前畸变校正中的元启发式SPGD算法

为了改善传统随机并行梯度下降(Stochastic Parallel Gradient Descent, SPGD)算法收敛速度慢且容易陷入局部极值的问题,提出了一种元启发式随机并行梯度下降(Meta-Heuristic SPGD, MHSPGD)算法。该算法将SPGD算法和元启发式算法的开发与探索两步结合,首先利用SPGD算法的梯度下降搜索得到局部最优解,然后进行邻域搜索得到局部最优区域以外的可能最优解,通过所有解性能指标的比较来确定新的迭代起点。随着搜索范围的自适应扩展,该算法能够避免陷入局部极值并趋向收敛于全局最优。同时,为了避免重复搜索,建立了记忆表来记录迭代过程中产生的次最优解。搭建了无波前探测器自适应光学系统模型,运用所提算法对不同湍流强度下的波前畸变进行了仿真校正,并针对不同Zernike阶数的像差进行了仿真实验。在三种湍流强度下,MHSPGD算法所能达到的斯特列尔比(Strehl Ratio, SR)分别为0.7621、0.6554、0.3749,相比于SPGD算法分别提升了0.1%、2%和18.6%。此外,当畸变中含有较多高阶成分时,文中所提优化算法相比传统的SPGD算法,SR收敛到0.6所需的迭代次数减少了约47%,且SR收敛极限值也提升了约9.4%。结果表明:与三种主流优化算法相比,MHSPGD在保持较快收敛速度的同时,能够在各种湍流强度下达到更高的收敛极限,有效地解决了算法的局部收敛问题。     

一种新型自适应最小均方算法

针对最小均方(LMS)算法应用于功率放大器时存在收敛速度与收敛精度相矛盾的问题,提出了基于步长比较的最小均方算法。在带有P因子的变步长最小均方算法的基础上,采用简化的Sigmoid函数对步长进行改进,通过对前后两次步长的比较来确定是否更新权系数,以误差的自相关时间均值及均方误差的时间均值来调节算法步长,可以加快算法的收敛速度,降低算法的计算量。仿真结果表明,与最小均方算法相比,经过自适应预失真处理后,该算法的误差向量幅度(EVM)值提高了2.653 2%,系统邻信道功率比(ACPR)减少了4 dB。     

DS/CDMA多用户检测盲自适应梯度算法的改进

本文在直接序列/码分多址(DS/CDMA)系统盲检测自适应梯度算法(BAG)的基础上,提出了一种多用户检测盲自适应梯度算法的改进算法(IBAG).BAG算法包括自适应梯度算法(TBAG)和时变自适应梯度算法(TIBAG),TBAG算法的跟踪特性较好,但起始收敛特性不好;TIBAG算法的收敛特性较好,但跟踪特性较差,几乎不具备跟踪能力.本改进算法结合了这两种自适应梯度算法的优点,在不增加算法复杂度的前提下,使其收敛性和跟踪特性较BAG算法有所提高.     

基于动态自适应双门限能量检测的序贯协作频谱感知算法

针对在一般高斯白噪声环境中,传统的双门限能量检测频谱感知算法忽略确定两门限之间的感知信息的问题,该文提出一种基于动态自适应双门限能量检测的序贯协作频谱感知算法。新算法以最优化检测概率为目标,采用序贯方式对协作用户进行动态自适应双门限建模,并对处于两门限之间的接收能量值进行软判决。进一步地,新算法能自适应动态调整门限大小和各判决区域协作用户数,以达到最大化检测概率和最优化受试工作特征曲线的目的。理论分析和仿真结果表明,与经典的双门限能量检测算法相比,提出算法具有更优的检测概率。     

Optimal variable step size for the LMS/Newton algorithm withapplication to subband adaptive filtering

An efficient approach for the computation of the optimum convergence factor for the LMS (least mean square)/Newton algorithm applied to a transversal FIR structure is proposed. The approach leads to a variable step size algorithm that results in a dramatic reduction in convergence time. The algorithm is evaluated in system identification applications where two alternative implementations of the adaptive filter are considered: the conventional transversal FIR realization and adaptive filtering in subbands     

RFID多读写器自适应加权功率分配算法

针对多读写器应用环境中的干扰问题,该文给出了基于误包率的信噪比估计式,分析了估计式的统计性能,提出了一种可自适应调节权重因子的比例公平功率分配算法。该算法使处于高密度区域的读写器能够自动增大权重因子,从而保证了工作区域内处于不同位置的读写器均能将读写质量维持在标称值附近。该文将分布式算法用于功率分配的具体实现,并证明了算法迭代值可收敛于某个局部最优解。仿真结果表明,算法在公平性方面要优于现有的功率分配方案,且具有良好的收敛性能。     

一种自适应分裂与合并的运动目标聚类分割算法

针对智能监控系统中多个运动目标进行图像分割这一问题,该文提出一种自适应分裂与合并的多运动目标聚类分割算法。该算法首先利用视频图像的时域信息,通过样本方差进行背景建模,分割出包含多个运动目标的前景图像。然后定义了像素点的空间连通率,并设计一种利用中垂线分割法,对初始聚类进行自适应分裂与合并。在无需事先设定聚类分割数目的条件下,自组织迭代聚类算法能完成多运动目标的分割。实验结果证明该算法对多运动目标分割效果好,分割结果与人眼视觉的判断一致。利用空间连通信息使得算法迭代收敛速度快,具有良好的实时性。     

A Unified Conjugate Gradient Based Approach for Optimal Complex Block FIR Filtering

This paper proposes a unified block based approach to generate two complex filtering algorithms. The proposed unified approach calculates the complex conjugate gradients as the orthogonal update directions for the adaptive filter coefficients at each iteration. Along each update direction, the time-varying convergence factors tailored for the adaptive filter coefficients are updated based on the complex Taylor series expansion. The general formulation leads to two classes of adaptive algorithms: the Complex Block Conjugate Least Mean Square algorithm with Individual adaptation of parameters, CBCI-LMS, and the Complex Block Conjugate Least Mean Square algorithm, CBC-LMS. The formulation shows that the CBCI-LMS algorithm achieves faster adaptation than the CBC-LMS technique at the expense of an increase in the number of computations per iteration. The performances of these two proposed algorithms are evaluated and compared to existing techniques. In addition, the implementation aspects are examined under a wide range of adaptive conditions. These two generated algorithms are then applied to channel equalization and adaptive array beamforming. Based on the obtained results, the proposed algorithms demonstrate excellent convergence characteristics, in terms of the adaptation speed and accuracy.     

基于自适应局部增强的手机TFT-LCD屏Mura缺陷自动检测

针对手机屏幕图像整体亮度不均以及Mura缺陷对比度低等特点,提出一种基于自适应局部增强的Mura缺陷自动在线检测方法。首先对CCD相机采集的手机屏幕原始图像进行感兴趣区域提取、几何校正、滤波等预处理,获取图像中的屏幕区域,然后将屏幕区域划分为多个不重叠的像素块,并根据每个像素块的灰度分布特征,采用自适应局部增强算法自动识别并定位图像中的Mura区域,最后考虑到Mura缺陷大小的不确定,提出采用多层级分块的方式对屏幕区域进行检测,提高算法鲁棒性。实验结果表明,相较现有多种屏幕缺陷自动检测算法,本文方法能更准确有效地识别手机屏幕中的Mura缺陷,且覆盖率和误检率分别为91.17%和5.84%。